AML/CFT/CPF endeavors in the crypto space: from blockchain analytics to machine learning

Abstract

Financial applications of distributed ledger technologies (DLTs) generate regulatory concerns. In the crypto sphere, pseudonymity may safeguard privacy and data protection, but lack of identifiability cripples investigation and enforcement. This challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT/CPF). Nonetheless, forensic techniques trace transfers across blockchain ecosystems and provide intelligence to regulated entities. This working paper addresses anomaly detection in the crypto space, the role of machine learning, and the impact of disintermediation